Map construction methods automatically produce and/or update street map datasets using
vehicle tracking data. Enabled by the ubiquitous generation of geo-referenced tracking data …

Inferring road graphs from satellite imagery is a challenging computer vision task. Prior
solutions fall into two categories:(1) pixel-wise segmentation-based approaches, which …

As a result of the availability of Global Positioning System (GPS) sensors in a variety of
everyday devices, GPS trace data are becoming increasingly abundant. One potential use of …

In order to facilitate the introduction of transit tracking and arrival time prediction in smaller
transit agencies, we investigate an automatic, smartphone-based system which we call …

This paper describes a process for automatically inferring maps from large collections of
opportunistically collected GPS traces. In this type of dataset, there is often a great disparity …

Deep learning is revolutionizing the map** industry. Under lightweight human curation,
computer has generated almost half of the roads in Thailand on Open-StreetMap (OSM) …

Motivated by new technologies, such as Internet of Things, Big Data, Cloud Computing, data-
driven based smart maritime related research is attracting more and more attention. With the …

We address the problem of inferring road maps from large-scale GPS traces that have
relatively low resolution and sampling frequency. Unlike past published work that requires …

The inaccuracy of manually created digital road maps is a persistent problem, despite their
high economic value. We present CrowdAtlas, which automates map update based on …

Digital maps with lane-level details are the foundation of many applications. However,
creating and maintaining digital maps especially maps with lane-level details, are labor …